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This is where I spent most of my time and still felt like I only got halfway through.
Start by framing the goal: to estimate the causal impact of the TV campaign on flu vaccinations by comparing test DMAs to matched control DMAs. Then walk through a structured process: define matching criteria, select 12 test DMAs and matched controls, choose a pre-period, and outline strategies to mitigate noise. Emphasize the importance of balance checks and sensitivity analyses.
Pro tip: Use a difference-in-differences design with synthetic control methods to strengthen causal inference, and pre-register your analysis plan to avoid p-hacking. Also, consider using placebo tests to validate that your matched controls show no effect pre-campaign.
Select relevant covariates such as historical flu vaccination rates, demographic composition (age, income, education), population size, and baseline TV viewership. Also include market-level factors like healthcare access and prior campaign exposure.
Use propensity score matching or coarsened exact matching to pair each test DMA with one or more control DMAs that are similar on the criteria. Ensure the 12 test DMAs are representative of the target audience and have sufficient population for statistical power.
Use a pre-period of at least 8-12 weeks (or multiple flu seasons if available) to establish baseline trends and account for seasonality. This allows for difference-in-differences analysis and checks for parallel trends.
Identify potential confounders like news cycles (e.g., flu outbreaks), sports events (e.g., NFL playoffs), and holidays (e.g., Thanksgiving). Use methods like including covariates for these events, matching on event timing, or using synthetic controls to adjust for their impact.
Conduct balance checks on pre-period outcomes and covariates. Perform placebo tests on pre-period to ensure no pre-existing differences. Use difference-in-differences or synthetic control methods to estimate the campaign effect, and run sensitivity analyses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I anchored on incremental verified vaccinations per DMA as the primary KPI, which felt right.
Start by clarifying the experiment's objective and unit of randomization, then define primary and secondary KPIs aligned with business goals. Address each challenge (spillover, unequal TRP, concurrent media) with specific statistical and design solutions, emphasizing trade-offs.
Pro tip: Incrementality is the ultimate goal; always consider intent-to-treat (ITT) analysis and complement with causal inference methods like difference-in-differences or synthetic control to handle real-world complexities.
Confirm the experiment's goal (e.g., measuring ad effectiveness) and the randomization unit (e.g., DMA, user). Discuss whether a geo-based or user-level design is appropriate.
Select primary KPIs (e.g., incremental sales, conversions) and secondary KPIs (e.g., brand lift, engagement). Ensure they are measurable, sensitive to the treatment, and aligned with business objectives.
Mitigate spillover by using geographically distant control DMAs, implementing a matched market design, or using statistical techniques like spatial regression. Consider user-level randomization if feasible.
Account for unequal TRP by stratifying randomization by TRP levels, using TRP as a covariate in analysis, or weighting observations. Ensure balance across treatment and control groups.
Identify concurrent media activities and either hold them constant, include them as covariates, or use a factorial design. Consider time-series methods to isolate the treatment effect.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I'm more comfortable on the measurement side than media planning so this exposed a gap.
Start by framing the campaign objectives and how they translate into GRP/TRP targets, daypart mix, and reach-frequency goals, emphasizing alignment with CVS Health's business goals. Then, explain your approach to modeling adstock decay and saturation effects, focusing on data-driven methods and validation. Conclude by discussing how you would optimize the media plan based on model outputs.
Pro tip: Demonstrate familiarity with CVS Health's retail media network and how GRP/TRP metrics connect to pharmacy and front-store sales, showing you understand their unique measurement challenges. Also, mention the importance of calibrating adstock and saturation parameters with experiments or holdout tests to avoid overfitting.
Clarify the campaign's business goals (e.g., awareness, consideration, sales) and how GRP/TRP targets support them. Discuss how daypart mix and reach-frequency goals are set based on target audience behavior and media consumption patterns.
Explain how you would determine GRP/TRP levels using historical performance, competitive benchmarks, and budget constraints. Describe how you allocate GRPs across dayparts to maximize reach and frequency against the target audience.
Detail how you balance reach and frequency to achieve effective frequency levels without oversaturating. Mention tools like reach curves and frequency distributions to optimize the media plan.
Describe adstock modeling: choose a decay function (e.g., geometric, exponential) and estimate decay rate using historical data or experiments. Discuss how adstock captures the carryover effect of advertising.
Explain saturation modeling: use a transformation (e.g., Hill function, log) to capture diminishing returns. Discuss how to estimate saturation parameters and incorporate them into media mix models for budget optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Power calc using market-level variance I knew cold.
Start by explaining how you would account for market-level variance in the power calculation, likely using cluster-level variance and intra-cluster correlation. Then outline the guardrails you would monitor to ensure experiment validity and safety. Finally, describe how you would triangulate results with MMM and pharmacy footfall data to validate findings and understand broader impact.
Pro tip: Emphasize that with geo-experiments, the unit of randomization is the market, so power depends on the number of markets and between-market variance, not individual users. Also, mention that guardrails should include both business metrics (e.g., sales, footfall) and health metrics (e.g., adverse events) to align with CVS Health's dual focus.
Clarify the geo-experiment design: markets as randomization units, treatment vs. control. Identify sources of market-level variance (e.g., demographics, baseline sales) and decide whether to use a matched-pair or stratified design to reduce variance.
Use the number of markets and estimate between-market variance (e.g., from historical data) to compute power. Account for intra-cluster correlation (ICC) if individual-level data is used, and consider using simulation or formulas for cluster-randomized trials.
Define guardrail metrics (e.g., overall sales, customer satisfaction, pharmacy footfall, adverse events) and set thresholds for acceptable variation. Implement real-time monitoring and stopping rules if guardrails are breached.
Use marketing mix modeling to estimate the expected lift from the intervention and compare with experimental results. Analyze pharmacy footfall data to see if the experiment impacted store visits, and check for consistency across data sources.
Synthesize findings from the experiment, MMM, and footfall data to draw robust conclusions. Discuss limitations, such as confounding or spillover effects, and recommend next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.